Overview

Dataset statistics

Number of variables30
Number of observations546279
Missing cells0
Missing cells (%)0.0%
Duplicate rows87775
Duplicate rows (%)16.1%
Total size in memory125.0 MiB
Average record size in memory240.0 B

Variable types

Numeric19
Categorical11

Alerts

Dataset has 87775 (16.1%) duplicate rowsDuplicates
days_from_first_active_until_booking is highly correlated with days_from_account_created_until_first_bookingHigh correlation
days_from_account_created_until_first_booking is highly correlated with days_from_first_active_until_bookingHigh correlation
year_first_active is highly correlated with year_first_booking and 1 other fieldsHigh correlation
month_first_active is highly correlated with week_of_year_first_active and 4 other fieldsHigh correlation
day_first_active is highly correlated with day_account_createdHigh correlation
day_of_week_first_active is highly correlated with day_of_week_account_createdHigh correlation
week_of_year_first_active is highly correlated with month_first_active and 4 other fieldsHigh correlation
year_first_booking is highly correlated with year_first_active and 1 other fieldsHigh correlation
month_first_booking is highly correlated with month_first_active and 4 other fieldsHigh correlation
week_of_year_first_booking is highly correlated with month_first_active and 4 other fieldsHigh correlation
year_account_created is highly correlated with year_first_active and 1 other fieldsHigh correlation
month_account_created is highly correlated with month_first_active and 4 other fieldsHigh correlation
day_account_created is highly correlated with day_first_activeHigh correlation
day_of_week_account_created is highly correlated with day_of_week_first_activeHigh correlation
week_of_year_account_created is highly correlated with month_first_active and 4 other fieldsHigh correlation
days_from_first_active_until_booking is highly correlated with days_from_account_created_until_first_booking and 1 other fieldsHigh correlation
days_from_account_created_until_first_booking is highly correlated with days_from_first_active_until_booking and 1 other fieldsHigh correlation
year_first_active is highly correlated with year_first_booking and 1 other fieldsHigh correlation
month_first_active is highly correlated with week_of_year_first_active and 4 other fieldsHigh correlation
day_first_active is highly correlated with day_account_createdHigh correlation
day_of_week_first_active is highly correlated with day_of_week_account_createdHigh correlation
week_of_year_first_active is highly correlated with month_first_active and 4 other fieldsHigh correlation
year_first_booking is highly correlated with days_from_first_active_until_booking and 3 other fieldsHigh correlation
month_first_booking is highly correlated with month_first_active and 4 other fieldsHigh correlation
week_of_year_first_booking is highly correlated with month_first_active and 4 other fieldsHigh correlation
year_account_created is highly correlated with year_first_active and 1 other fieldsHigh correlation
month_account_created is highly correlated with month_first_active and 4 other fieldsHigh correlation
day_account_created is highly correlated with day_first_activeHigh correlation
day_of_week_account_created is highly correlated with day_of_week_first_activeHigh correlation
week_of_year_account_created is highly correlated with month_first_active and 4 other fieldsHigh correlation
days_from_first_active_until_booking is highly correlated with days_from_account_created_until_first_bookingHigh correlation
days_from_account_created_until_first_booking is highly correlated with days_from_first_active_until_bookingHigh correlation
year_first_active is highly correlated with year_first_booking and 1 other fieldsHigh correlation
month_first_active is highly correlated with week_of_year_first_active and 4 other fieldsHigh correlation
day_first_active is highly correlated with day_account_createdHigh correlation
day_of_week_first_active is highly correlated with day_of_week_account_createdHigh correlation
week_of_year_first_active is highly correlated with month_first_active and 4 other fieldsHigh correlation
year_first_booking is highly correlated with year_first_active and 1 other fieldsHigh correlation
month_first_booking is highly correlated with month_first_active and 4 other fieldsHigh correlation
week_of_year_first_booking is highly correlated with month_first_active and 4 other fieldsHigh correlation
year_account_created is highly correlated with year_first_active and 1 other fieldsHigh correlation
month_account_created is highly correlated with month_first_active and 4 other fieldsHigh correlation
day_account_created is highly correlated with day_first_activeHigh correlation
day_of_week_account_created is highly correlated with day_of_week_first_activeHigh correlation
week_of_year_account_created is highly correlated with month_first_active and 4 other fieldsHigh correlation
affiliate_provider is highly correlated with affiliate_channelHigh correlation
first_browser is highly correlated with first_device_typeHigh correlation
signup_app is highly correlated with first_device_typeHigh correlation
affiliate_channel is highly correlated with affiliate_providerHigh correlation
first_device_type is highly correlated with first_browser and 1 other fieldsHigh correlation
signup_flow is highly correlated with affiliate_channel and 3 other fieldsHigh correlation
days_from_first_active_until_booking is highly correlated with days_from_account_created_until_first_booking and 5 other fieldsHigh correlation
days_from_account_created_until_first_booking is highly correlated with days_from_first_active_until_booking and 7 other fieldsHigh correlation
year_first_active is highly correlated with days_from_account_created_until_first_booking and 6 other fieldsHigh correlation
month_first_active is highly correlated with year_first_active and 6 other fieldsHigh correlation
day_first_active is highly correlated with day_first_booking and 1 other fieldsHigh correlation
day_of_week_first_active is highly correlated with day_of_week_account_createdHigh correlation
week_of_year_first_active is highly correlated with year_first_active and 6 other fieldsHigh correlation
year_first_booking is highly correlated with days_from_first_active_until_booking and 7 other fieldsHigh correlation
month_first_booking is highly correlated with days_from_first_active_until_booking and 7 other fieldsHigh correlation
day_first_booking is highly correlated with days_from_first_active_until_booking and 6 other fieldsHigh correlation
week_of_year_first_booking is highly correlated with days_from_first_active_until_booking and 9 other fieldsHigh correlation
year_account_created is highly correlated with days_from_account_created_until_first_booking and 6 other fieldsHigh correlation
month_account_created is highly correlated with year_first_active and 6 other fieldsHigh correlation
day_account_created is highly correlated with day_first_active and 1 other fieldsHigh correlation
day_of_week_account_created is highly correlated with day_of_week_first_activeHigh correlation
week_of_year_account_created is highly correlated with year_first_active and 6 other fieldsHigh correlation
affiliate_channel is highly correlated with signup_flow and 3 other fieldsHigh correlation
affiliate_provider is highly correlated with signup_flow and 2 other fieldsHigh correlation
first_affiliate_tracked is highly correlated with affiliate_channel and 1 other fieldsHigh correlation
signup_app is highly correlated with signup_flow and 3 other fieldsHigh correlation
first_device_type is highly correlated with signup_app and 1 other fieldsHigh correlation
first_browser is highly correlated with signup_flow and 2 other fieldsHigh correlation
country_destination is highly correlated with days_from_first_active_until_booking and 4 other fieldsHigh correlation
days_from_first_active_until_account_created is highly skewed (γ1 = 68.1409591) Skewed
signup_flow has 434571 (79.6%) zeros Zeros
days_from_first_active_until_booking has 120474 (22.1%) zeros Zeros
days_from_first_active_until_account_created has 545277 (99.8%) zeros Zeros
days_from_account_created_until_first_booking has 120411 (22.0%) zeros Zeros
day_of_week_first_active has 85482 (15.6%) zeros Zeros
day_of_week_first_booking has 122976 (22.5%) zeros Zeros
day_of_week_account_created has 85435 (15.6%) zeros Zeros

Reproduction

Analysis started2021-10-23 04:53:08.152220
Analysis finished2021-10-23 04:55:12.571403
Duration2 minutes and 4.42 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

age
Real number (ℝ≥0)

Distinct99
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.78003548
Minimum16
Maximum115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2021-10-23T01:55:12.658506image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile23
Q128
median33
Q341
95-th percentile61
Maximum115
Range99
Interquartile range (IQR)13

Descriptive statistics

Standard deviation13.19491544
Coefficient of variation (CV)0.3587521129
Kurtosis6.785259131
Mean36.78003548
Median Absolute Deviation (MAD)6
Skewness2.111932589
Sum20092161
Variance174.1057934
MonotonicityNot monotonic
2021-10-23T01:55:12.757212image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3129815
 
5.5%
3029770
 
5.4%
2928661
 
5.2%
3228337
 
5.2%
2826815
 
4.9%
3326116
 
4.8%
2724152
 
4.4%
3423507
 
4.3%
3521945
 
4.0%
2621221
 
3.9%
Other values (89)285940
52.3%
ValueCountFrequency (%)
1626
 
< 0.1%
1774
 
< 0.1%
181670
 
0.3%
193686
 
0.7%
202771
 
0.5%
214847
 
0.9%
228493
1.6%
2311818
2.2%
2414816
2.7%
2518788
3.4%
ValueCountFrequency (%)
11512
 
< 0.1%
1134
 
< 0.1%
1121
 
< 0.1%
1112
 
< 0.1%
110191
 
< 0.1%
10997
 
< 0.1%
10883
 
< 0.1%
10783
 
< 0.1%
10671
 
< 0.1%
1054099
0.8%

signup_flow
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.737092218
Minimum0
Maximum25
Zeros434571
Zeros (%)79.6%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2021-10-23T01:55:12.840681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile14
Maximum25
Range25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.311753542
Coefficient of variation (CV)3.057841999
Kurtosis11.34789856
Mean1.737092218
Median Absolute Deviation (MAD)0
Skewness3.506835154
Sum948937
Variance28.21472569
MonotonicityNot monotonic
2021-10-23T01:55:12.919615image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0434571
79.6%
127888
 
5.1%
222776
 
4.2%
2511426
 
2.1%
39877
 
1.8%
128243
 
1.5%
246559
 
1.2%
232831
 
0.5%
52086
 
0.4%
62005
 
0.4%
Other values (16)18017
 
3.3%
ValueCountFrequency (%)
0434571
79.6%
127888
 
5.1%
222776
 
4.2%
39877
 
1.8%
41984
 
0.4%
52086
 
0.4%
62005
 
0.4%
71799
 
0.3%
81819
 
0.3%
91621
 
0.3%
ValueCountFrequency (%)
2511426
2.1%
246559
1.2%
232831
 
0.5%
22796
 
0.1%
211046
 
0.2%
20655
 
0.1%
19716
 
0.1%
18665
 
0.1%
17703
 
0.1%
16706
 
0.1%

days_from_first_active_until_booking
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1853
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.4462738
Minimum0
Maximum2228
Zeros120474
Zeros (%)22.1%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2021-10-23T01:55:13.008695image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q3105
95-th percentile675
Maximum2228
Range2228
Interquartile range (IQR)104

Descriptive statistics

Standard deviation241.585994
Coefficient of variation (CV)2.074656286
Kurtosis9.684070986
Mean116.4462738
Median Absolute Deviation (MAD)6
Skewness2.960146322
Sum63612154
Variance58363.79247
MonotonicityNot monotonic
2021-10-23T01:55:13.101490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0120474
22.1%
169034
 
12.6%
232199
 
5.9%
320580
 
3.8%
415089
 
2.8%
511521
 
2.1%
610281
 
1.9%
78860
 
1.6%
87636
 
1.4%
96039
 
1.1%
Other values (1843)244566
44.8%
ValueCountFrequency (%)
0120474
22.1%
169034
12.6%
232199
 
5.9%
320580
 
3.8%
415089
 
2.8%
511521
 
2.1%
610281
 
1.9%
78860
 
1.6%
87636
 
1.4%
96039
 
1.1%
ValueCountFrequency (%)
22281
< 0.1%
20012
< 0.1%
19991
< 0.1%
19951
< 0.1%
19921
< 0.1%
19912
< 0.1%
19902
< 0.1%
19801
< 0.1%
19791
< 0.1%
19771
< 0.1%

days_from_first_active_until_account_created
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct327
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.226173805
Minimum0
Maximum1456
Zeros545277
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2021-10-23T01:55:13.195969image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1456
Range1456
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.19124704
Coefficient of variation (CV)45.0593606
Kurtosis5805.244583
Mean0.226173805
Median Absolute Deviation (MAD)0
Skewness68.1409591
Sum123554
Variance103.8615162
MonotonicityNot monotonic
2021-10-23T01:55:13.289396image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0545277
99.8%
2103
 
< 0.1%
191
 
< 0.1%
367
 
< 0.1%
461
 
< 0.1%
515
 
< 0.1%
614
 
< 0.1%
1312
 
< 0.1%
2910
 
< 0.1%
109
 
< 0.1%
Other values (317)620
 
0.1%
ValueCountFrequency (%)
0545277
99.8%
191
 
< 0.1%
2103
 
< 0.1%
367
 
< 0.1%
461
 
< 0.1%
515
 
< 0.1%
614
 
< 0.1%
75
 
< 0.1%
84
 
< 0.1%
98
 
< 0.1%
ValueCountFrequency (%)
14561
< 0.1%
13691
< 0.1%
13611
< 0.1%
11481
< 0.1%
10361
< 0.1%
10111
< 0.1%
10061
< 0.1%
9911
< 0.1%
9841
< 0.1%
9681
< 0.1%

days_from_account_created_until_first_booking
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1971
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.2196222
Minimum-349
Maximum2001
Zeros120411
Zeros (%)22.0%
Negative232
Negative (%)< 0.1%
Memory size4.2 MiB
2021-10-23T01:55:13.386252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-349
5-th percentile0
Q11
median6
Q3104
95-th percentile674
Maximum2001
Range2350
Interquartile range (IQR)103

Descriptive statistics

Standard deviation241.3262741
Coefficient of variation (CV)2.07646755
Kurtosis9.698711254
Mean116.2196222
Median Absolute Deviation (MAD)6
Skewness2.962090965
Sum63488339
Variance58238.37059
MonotonicityNot monotonic
2021-10-23T01:55:13.483335image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0120411
22.0%
168991
 
12.6%
232181
 
5.9%
320569
 
3.8%
415077
 
2.8%
511526
 
2.1%
610289
 
1.9%
78861
 
1.6%
87640
 
1.4%
96039
 
1.1%
Other values (1961)244695
44.8%
ValueCountFrequency (%)
-3491
< 0.1%
-3471
< 0.1%
-3381
< 0.1%
-3081
< 0.1%
-2981
< 0.1%
-2951
< 0.1%
-2881
< 0.1%
-2731
< 0.1%
-2691
< 0.1%
-2611
< 0.1%
ValueCountFrequency (%)
20012
< 0.1%
19991
< 0.1%
19951
< 0.1%
19921
< 0.1%
19912
< 0.1%
19902
< 0.1%
19801
< 0.1%
19791
< 0.1%
19771
< 0.1%
19761
< 0.1%

year_first_active
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.696038
Minimum2009
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2021-10-23T01:55:13.557731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2011
Q12012
median2013
Q32013
95-th percentile2014
Maximum2014
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9287412643
Coefficient of variation (CV)0.0004614413934
Kurtosis-0.3225380112
Mean2012.696038
Median Absolute Deviation (MAD)1
Skewness-0.3714002799
Sum1099493579
Variance0.8625603361
MonotonicityNot monotonic
2021-10-23T01:55:13.623956image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2013220902
40.4%
2012159773
29.2%
2014110344
20.2%
201149170
 
9.0%
20106081
 
1.1%
20099
 
< 0.1%
ValueCountFrequency (%)
20099
 
< 0.1%
20106081
 
1.1%
201149170
 
9.0%
2012159773
29.2%
2013220902
40.4%
2014110344
20.2%
ValueCountFrequency (%)
2014110344
20.2%
2013220902
40.4%
2012159773
29.2%
201149170
 
9.0%
20106081
 
1.1%
20099
 
< 0.1%

month_first_active
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.806186948
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2021-10-23T01:55:13.692699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.064040489
Coefficient of variation (CV)0.5277199161
Kurtosis-0.8653276745
Mean5.806186948
Median Absolute Deviation (MAD)2
Skewness0.2761994348
Sum3171798
Variance9.38834412
MonotonicityNot monotonic
2021-10-23T01:55:13.761677image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
574458
13.6%
670701
12.9%
461420
11.2%
351173
9.4%
245375
8.3%
843397
7.9%
143202
7.9%
940490
7.4%
733307
6.1%
1032058
5.9%
Other values (2)50698
9.3%
ValueCountFrequency (%)
143202
7.9%
245375
8.3%
351173
9.4%
461420
11.2%
574458
13.6%
670701
12.9%
733307
6.1%
843397
7.9%
940490
7.4%
1032058
5.9%
ValueCountFrequency (%)
1219158
 
3.5%
1131540
5.8%
1032058
5.9%
940490
7.4%
843397
7.9%
733307
6.1%
670701
12.9%
574458
13.6%
461420
11.2%
351173
9.4%

day_first_active
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.51226388
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2021-10-23T01:55:13.838745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.42517705
Coefficient of variation (CV)0.5431300754
Kurtosis-1.179430597
Mean15.51226388
Median Absolute Deviation (MAD)7
Skewness-0.0007476385669
Sum8474024
Variance70.98360832
MonotonicityNot monotonic
2021-10-23T01:55:13.923413image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2420006
 
3.7%
2219954
 
3.7%
2319672
 
3.6%
2719473
 
3.6%
1319391
 
3.5%
1619358
 
3.5%
319248
 
3.5%
1519069
 
3.5%
1018974
 
3.5%
2118879
 
3.5%
Other values (21)352255
64.5%
ValueCountFrequency (%)
112795
2.3%
216126
3.0%
319248
3.5%
418659
3.4%
518667
3.4%
618398
3.4%
718646
3.4%
818542
3.4%
918857
3.5%
1018974
3.5%
ValueCountFrequency (%)
312843
 
0.5%
3011313
2.1%
2914627
2.7%
2817372
3.2%
2719473
3.6%
2618169
3.3%
2518742
3.4%
2420006
3.7%
2319672
3.6%
2219954
3.7%

day_of_week_first_active
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.44213671
Minimum0
Maximum6
Zeros85482
Zeros (%)15.6%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2021-10-23T01:55:13.999108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.732002571
Coefficient of variation (CV)0.7092160581
Kurtosis-0.9209470515
Mean2.44213671
Median Absolute Deviation (MAD)1
Skewness0.2732539292
Sum1334088
Variance2.999832906
MonotonicityNot monotonic
2021-10-23T01:55:14.060569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2105352
19.3%
1103305
18.9%
393280
17.1%
085482
15.6%
477853
14.3%
557215
10.5%
623792
 
4.4%
ValueCountFrequency (%)
085482
15.6%
1103305
18.9%
2105352
19.3%
393280
17.1%
477853
14.3%
557215
10.5%
623792
 
4.4%
ValueCountFrequency (%)
623792
 
4.4%
557215
10.5%
477853
14.3%
393280
17.1%
2105352
19.3%
1103305
18.9%
085482
15.6%

week_of_year_first_active
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.02255807
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2021-10-23T01:55:14.146899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q114
median23
Q334
95-th percentile48
Maximum53
Range52
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.29538283
Coefficient of variation (CV)0.5534540823
Kurtosis-0.8708825582
Mean24.02255807
Median Absolute Deviation (MAD)10
Skewness0.2751285319
Sum13123019
Variance176.7672046
MonotonicityNot monotonic
2021-10-23T01:55:14.248211image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2318592
 
3.4%
2117336
 
3.2%
2517012
 
3.1%
1916968
 
3.1%
2016961
 
3.1%
2416923
 
3.1%
2616802
 
3.1%
1815515
 
2.8%
2215352
 
2.8%
1714939
 
2.7%
Other values (43)379879
69.5%
ValueCountFrequency (%)
15764
1.1%
26395
1.2%
39919
1.8%
410171
1.9%
59358
1.7%
611323
2.1%
711283
2.1%
811470
2.1%
911266
2.1%
1011789
2.2%
ValueCountFrequency (%)
532
 
< 0.1%
523262
0.6%
515131
0.9%
506159
1.1%
496992
1.3%
486224
1.1%
477396
1.4%
468149
1.5%
458008
1.5%
446755
1.2%

year_first_booking
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.005448
Minimum2010
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2021-10-23T01:55:14.326756image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2011
Q12012
median2013
Q32014
95-th percentile2015
Maximum2015
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.115803619
Coefficient of variation (CV)0.0005542973666
Kurtosis-0.3914133732
Mean2013.005448
Median Absolute Deviation (MAD)1
Skewness0.002770402015
Sum1099662603
Variance1.245017715
MonotonicityNot monotonic
2021-10-23T01:55:14.398602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2013199068
36.4%
2012134766
24.7%
2014109683
20.1%
201559628
 
10.9%
201138205
 
7.0%
20104929
 
0.9%
ValueCountFrequency (%)
20104929
 
0.9%
201138205
 
7.0%
2012134766
24.7%
2013199068
36.4%
2014109683
20.1%
201559628
 
10.9%
ValueCountFrequency (%)
201559628
 
10.9%
2014109683
20.1%
2013199068
36.4%
2012134766
24.7%
201138205
 
7.0%
20104929
 
0.9%

month_first_booking
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.896624252
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2021-10-23T01:55:14.470956image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.78863546
Coefficient of variation (CV)0.4729206646
Kurtosis-0.5811415308
Mean5.896624252
Median Absolute Deviation (MAD)2
Skewness0.2078363938
Sum3221202
Variance7.776487731
MonotonicityNot monotonic
2021-10-23T01:55:14.539724image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6122412
22.4%
566871
12.2%
451430
9.4%
344936
 
8.2%
842984
 
7.9%
739742
 
7.3%
239337
 
7.2%
937727
 
6.9%
132093
 
5.9%
1029770
 
5.4%
Other values (2)38977
 
7.1%
ValueCountFrequency (%)
132093
 
5.9%
239337
 
7.2%
344936
 
8.2%
451430
9.4%
566871
12.2%
6122412
22.4%
739742
 
7.3%
842984
 
7.9%
937727
 
6.9%
1029770
 
5.4%
ValueCountFrequency (%)
1213024
 
2.4%
1125953
 
4.8%
1029770
 
5.4%
937727
 
6.9%
842984
 
7.9%
739742
 
7.3%
6122412
22.4%
566871
12.2%
451430
9.4%
344936
 
8.2%

day_first_booking
Real number (ℝ≥0)

HIGH CORRELATION

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.58056964
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2021-10-23T01:55:14.616170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median17
Q325
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.890933776
Coefficient of variation (CV)0.5362260749
Kurtosis-1.250846033
Mean16.58056964
Median Absolute Deviation (MAD)8
Skewness-0.0815341159
Sum9057617
Variance79.0487034
MonotonicityNot monotonic
2021-10-23T01:55:14.697947image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2966746
 
12.2%
1518369
 
3.4%
1618127
 
3.3%
518061
 
3.3%
1717815
 
3.3%
2317738
 
3.2%
1117555
 
3.2%
417546
 
3.2%
917401
 
3.2%
1317363
 
3.2%
Other values (21)319558
58.5%
ValueCountFrequency (%)
112871
2.4%
214764
2.7%
316673
3.1%
417546
3.2%
518061
3.3%
616794
3.1%
716317
3.0%
816438
3.0%
917401
3.2%
1016944
3.1%
ValueCountFrequency (%)
312028
 
0.4%
307800
 
1.4%
2966746
12.2%
2814067
 
2.6%
2714972
 
2.7%
2615497
 
2.8%
2517109
 
3.1%
2417242
 
3.2%
2317738
 
3.2%
2217196
 
3.1%

day_of_week_first_booking
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.198726292
Minimum0
Maximum6
Zeros122976
Zeros (%)22.5%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2021-10-23T01:55:14.773072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.740718929
Coefficient of variation (CV)0.7916942348
Kurtosis-0.9350392808
Mean2.198726292
Median Absolute Deviation (MAD)1
Skewness0.3442726796
Sum1201118
Variance3.030102391
MonotonicityNot monotonic
2021-10-23T01:55:14.835827image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0122976
22.5%
299818
18.3%
195110
17.4%
389558
16.4%
472818
13.3%
549568
9.1%
616431
 
3.0%
ValueCountFrequency (%)
0122976
22.5%
195110
17.4%
299818
18.3%
389558
16.4%
472818
13.3%
549568
9.1%
616431
 
3.0%
ValueCountFrequency (%)
616431
 
3.0%
549568
9.1%
472818
13.3%
389558
16.4%
299818
18.3%
195110
17.4%
0122976
22.5%

week_of_year_first_booking
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.66208476
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2021-10-23T01:55:14.918819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q116
median25
Q333
95-th percentile46
Maximum52
Range51
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.15744672
Coefficient of variation (CV)0.4929610305
Kurtosis-0.6199561973
Mean24.66208476
Median Absolute Deviation (MAD)9
Skewness0.1555659313
Sum13472379
Variance147.8035107
MonotonicityNot monotonic
2021-10-23T01:55:15.018941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2766130
 
12.1%
2417404
 
3.2%
2316869
 
3.1%
2116335
 
3.0%
2515682
 
2.9%
2615175
 
2.8%
2014801
 
2.7%
1914599
 
2.7%
2213686
 
2.5%
1813617
 
2.5%
Other values (42)341981
62.6%
ValueCountFrequency (%)
14045
 
0.7%
24745
0.9%
37661
1.4%
47974
1.5%
57177
1.3%
69017
1.7%
79476
1.7%
810118
1.9%
99806
1.8%
1010369
1.9%
ValueCountFrequency (%)
521775
 
0.3%
513854
0.7%
504770
0.9%
495604
1.0%
485099
0.9%
475790
1.1%
466480
1.2%
456863
1.3%
445632
1.0%
436086
1.1%

year_account_created
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2013
220953 
2012
159786 
2014
110385 
2011
49108 
2010
 
6047

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2011
2nd row2010
3rd row2011
4th row2010
5th row2010

Common Values

ValueCountFrequency (%)
2013220953
40.4%
2012159786
29.2%
2014110385
20.2%
201149108
 
9.0%
20106047
 
1.1%

Length

2021-10-23T01:55:15.102187image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-23T01:55:15.151027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2013220953
40.4%
2012159786
29.2%
2014110385
20.2%
201149108
 
9.0%
20106047
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

month_account_created
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.807395122
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2021-10-23T01:55:15.223083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.064773168
Coefficient of variation (CV)0.5277362921
Kurtosis-0.8662793891
Mean5.807395122
Median Absolute Deviation (MAD)2
Skewness0.2756048808
Sum3172458
Variance9.392834574
MonotonicityNot monotonic
2021-10-23T01:55:15.291163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
574443
13.6%
670662
12.9%
461361
11.2%
351150
9.4%
245364
8.3%
843420
7.9%
143221
7.9%
940494
7.4%
733322
6.1%
1032057
5.9%
Other values (2)50785
9.3%
ValueCountFrequency (%)
143221
7.9%
245364
8.3%
351150
9.4%
461361
11.2%
574443
13.6%
670662
12.9%
733322
6.1%
843420
7.9%
940494
7.4%
1032057
5.9%
ValueCountFrequency (%)
1219163
 
3.5%
1131622
5.8%
1032057
5.9%
940494
7.4%
843420
7.9%
733322
6.1%
670662
12.9%
574443
13.6%
461361
11.2%
351150
9.4%

day_account_created
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.51420428
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2021-10-23T01:55:15.368743image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.426259432
Coefficient of variation (CV)0.5431319119
Kurtosis-1.179780674
Mean15.51420428
Median Absolute Deviation (MAD)7
Skewness-0.001108146792
Sum8475084
Variance71.00184802
MonotonicityNot monotonic
2021-10-23T01:55:15.452183image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2420025
 
3.7%
2219978
 
3.7%
2319644
 
3.6%
2719491
 
3.6%
1619380
 
3.5%
1319380
 
3.5%
319254
 
3.5%
1519070
 
3.5%
1018986
 
3.5%
918876
 
3.5%
Other values (21)352195
64.5%
ValueCountFrequency (%)
112800
2.3%
216128
3.0%
319254
3.5%
418663
3.4%
518659
3.4%
618378
3.4%
718635
3.4%
818547
3.4%
918876
3.5%
1018986
3.5%
ValueCountFrequency (%)
312842
 
0.5%
3011307
2.1%
2914621
2.7%
2817435
3.2%
2719491
3.6%
2618169
3.3%
2518743
3.4%
2420025
3.7%
2319644
3.6%
2219978
3.7%

day_of_week_account_created
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.442533943
Minimum0
Maximum6
Zeros85435
Zeros (%)15.6%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2021-10-23T01:55:15.527020image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.732170097
Coefficient of variation (CV)0.7091693042
Kurtosis-0.9214786044
Mean2.442533943
Median Absolute Deviation (MAD)1
Skewness0.2732217998
Sum1334305
Variance3.000413245
MonotonicityNot monotonic
2021-10-23T01:55:15.588553image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2105409
19.3%
1103329
18.9%
393136
17.0%
085435
15.6%
477903
14.3%
557264
10.5%
623803
 
4.4%
ValueCountFrequency (%)
085435
15.6%
1103329
18.9%
2105409
19.3%
393136
17.0%
477903
14.3%
557264
10.5%
623803
 
4.4%
ValueCountFrequency (%)
623803
 
4.4%
557264
10.5%
477903
14.3%
393136
17.0%
2105409
19.3%
1103329
18.9%
085435
15.6%

week_of_year_account_created
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.02836097
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2021-10-23T01:55:15.670155image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q114
median23
Q334
95-th percentile48
Maximum53
Range52
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.29918669
Coefficient of variation (CV)0.5534787292
Kurtosis-0.8720709604
Mean24.02836097
Median Absolute Deviation (MAD)10
Skewness0.2745425759
Sum13126189
Variance176.8683667
MonotonicityNot monotonic
2021-10-23T01:55:15.772984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2318589
 
3.4%
2117345
 
3.2%
2517014
 
3.1%
1916951
 
3.1%
2016931
 
3.1%
2416914
 
3.1%
2616794
 
3.1%
1815518
 
2.8%
2215362
 
2.8%
1714928
 
2.7%
Other values (43)379933
69.5%
ValueCountFrequency (%)
15766
1.1%
26396
1.2%
39917
1.8%
410172
1.9%
59368
1.7%
611326
2.1%
711288
2.1%
811461
2.1%
911265
2.1%
1011786
2.2%
ValueCountFrequency (%)
532
 
< 0.1%
523263
 
0.6%
515132
0.9%
506160
1.1%
496994
1.3%
486226
1.1%
477460
1.4%
468165
1.5%
458016
1.5%
446757
1.2%

gender
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
FEMALE
243211 
MALE
216842 
-unknown-
84626 
OTHER
 
1600

Length

Max length9
Median length6
Mean length5.667924266
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMALE
2nd rowFEMALE
3rd rowFEMALE
4th row-unknown-
5th rowFEMALE

Common Values

ValueCountFrequency (%)
FEMALE243211
44.5%
MALE216842
39.7%
-unknown-84626
 
15.5%
OTHER1600
 
0.3%

Length

2021-10-23T01:55:15.860622image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-23T01:55:15.909905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
female243211
44.5%
male216842
39.7%
unknown84626
 
15.5%
other1600
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

signup_method
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
basic
342438 
facebook
203334 
google
 
507

Length

Max length8
Median length5
Mean length6.117577282
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfacebook
2nd rowbasic
3rd rowfacebook
4th rowbasic
5th rowbasic

Common Values

ValueCountFrequency (%)
basic342438
62.7%
facebook203334
37.2%
google507
 
0.1%

Length

2021-10-23T01:55:15.970175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-23T01:55:16.021250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
basic342438
62.7%
facebook203334
37.2%
google507
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

language
Categorical

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
en
531343 
fr
 
3510
es
 
2500
de
 
2362
zh
 
1761
Other values (20)
 
4803

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowen
2nd rowen
3rd rowen
4th rowen
5th rowen

Common Values

ValueCountFrequency (%)
en531343
97.3%
fr3510
 
0.6%
es2500
 
0.5%
de2362
 
0.4%
zh1761
 
0.3%
it1037
 
0.2%
ko975
 
0.2%
ru686
 
0.1%
nl386
 
0.1%
pt380
 
0.1%
Other values (15)1339
 
0.2%

Length

2021-10-23T01:55:16.072359image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
en531343
97.3%
fr3510
 
0.6%
es2500
 
0.5%
de2362
 
0.4%
zh1761
 
0.3%
it1037
 
0.2%
ko975
 
0.2%
ru686
 
0.1%
nl386
 
0.1%
pt380
 
0.1%
Other values (15)1339
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

affiliate_channel
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
direct
363423 
sem-brand
72963 
sem-non-brand
47509 
seo
 
24159
other
 
15986
Other values (3)
 
22239

Length

Max length13
Median length6
Mean length6.806657404
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowseo
2nd rowdirect
3rd rowdirect
4th rowdirect
5th rowother

Common Values

ValueCountFrequency (%)
direct363423
66.5%
sem-brand72963
 
13.4%
sem-non-brand47509
 
8.7%
seo24159
 
4.4%
other15986
 
2.9%
api13862
 
2.5%
content5657
 
1.0%
remarketing2720
 
0.5%

Length

2021-10-23T01:55:16.144162image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-23T01:55:16.198927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
direct363423
66.5%
sem-brand72963
 
13.4%
sem-non-brand47509
 
8.7%
seo24159
 
4.4%
other15986
 
2.9%
api13862
 
2.5%
content5657
 
1.0%
remarketing2720
 
0.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

affiliate_provider
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
direct
362413 
google
137094 
other
 
22322
facebook
 
6818
craigslist
 
6206
Other values (12)
 
11426

Length

Max length19
Median length6
Mean length6.051290641
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgoogle
2nd rowdirect
3rd rowdirect
4th rowdirect
5th rowcraigslist

Common Values

ValueCountFrequency (%)
direct362413
66.3%
google137094
 
25.1%
other22322
 
4.1%
facebook6818
 
1.2%
craigslist6206
 
1.1%
bing5613
 
1.0%
facebook-open-graph1716
 
0.3%
vast1296
 
0.2%
padmapper967
 
0.2%
yahoo739
 
0.1%
Other values (7)1095
 
0.2%

Length

2021-10-23T01:55:16.276401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
direct362413
66.3%
google137094
 
25.1%
other22322
 
4.1%
facebook6818
 
1.2%
craigslist6206
 
1.1%
bing5613
 
1.0%
facebook-open-graph1716
 
0.3%
vast1296
 
0.2%
padmapper967
 
0.2%
yahoo739
 
0.1%
Other values (7)1095
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

first_affiliate_tracked
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
untracked
298351 
linked
120133 
omg
111276 
tracked-other
 
12357
product
 
3842
Other values (2)
 
320

Length

Max length13
Median length9
Mean length7.194492192
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowuntracked
2nd rowuntracked
3rd rowuntracked
4th rowuntracked
5th rowuntracked

Common Values

ValueCountFrequency (%)
untracked298351
54.6%
linked120133
22.0%
omg111276
 
20.4%
tracked-other12357
 
2.3%
product3842
 
0.7%
marketing243
 
< 0.1%
local ops77
 
< 0.1%

Length

2021-10-23T01:55:16.353212image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-23T01:55:16.404922image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
untracked298351
54.6%
linked120133
22.0%
omg111276
 
20.4%
tracked-other12357
 
2.3%
product3842
 
0.7%
marketing243
 
< 0.1%
local77
 
< 0.1%
ops77
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

signup_app
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
Web
504897 
iOS
 
27946
Moweb
 
7662
Android
 
5774

Length

Max length7
Median length3
Mean length3.070330362
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWeb
2nd rowWeb
3rd rowWeb
4th rowWeb
5th rowWeb

Common Values

ValueCountFrequency (%)
Web504897
92.4%
iOS27946
 
5.1%
Moweb7662
 
1.4%
Android5774
 
1.1%

Length

2021-10-23T01:55:16.477855image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-23T01:55:16.532104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
web504897
92.4%
ios27946
 
5.1%
moweb7662
 
1.4%
android5774
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

first_device_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
Mac Desktop
282841 
Windows Desktop
181356 
iPad
34821 
iPhone
31998 
Other/Unknown
 
5982
Other values (4)
 
9281

Length

Max length18
Median length11
Mean length11.66326364
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMac Desktop
2nd rowWindows Desktop
3rd rowMac Desktop
4th rowMac Desktop
5th rowMac Desktop

Common Values

ValueCountFrequency (%)
Mac Desktop282841
51.8%
Windows Desktop181356
33.2%
iPad34821
 
6.4%
iPhone31998
 
5.9%
Other/Unknown5982
 
1.1%
Desktop (Other)3596
 
0.7%
Android Phone3031
 
0.6%
Android Tablet2587
 
0.5%
SmartPhone (Other)67
 
< 0.1%

Length

2021-10-23T01:55:16.593659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-23T01:55:16.653267image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
desktop467793
45.9%
mac282841
27.7%
windows181356
 
17.8%
ipad34821
 
3.4%
iphone31998
 
3.1%
other/unknown5982
 
0.6%
android5618
 
0.6%
other3663
 
0.4%
phone3031
 
0.3%
tablet2587
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

first_browser
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct41
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
Chrome
191007 
Safari
133717 
Firefox
98677 
IE
44859 
Mobile Safari
42069 
Other values (36)
35950 

Length

Max length18
Median length6
Mean length6.614079985
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowChrome
2nd rowIE
3rd rowFirefox
4th rowChrome
5th rowSafari

Common Values

ValueCountFrequency (%)
Chrome191007
35.0%
Safari133717
24.5%
Firefox98677
18.1%
IE44859
 
8.2%
Mobile Safari42069
 
7.7%
-unknown-30873
 
5.7%
Chrome Mobile2131
 
0.4%
Android Browser1124
 
0.2%
Opera419
 
0.1%
Chromium273
 
< 0.1%
Other values (31)1130
 
0.2%

Length

2021-10-23T01:55:16.741365image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chrome193138
32.6%
safari175786
29.7%
firefox98716
16.7%
ie44872
 
7.6%
mobile44254
 
7.5%
unknown30873
 
5.2%
browser1218
 
0.2%
android1124
 
0.2%
opera423
 
0.1%
chromium273
 
< 0.1%
Other values (33)1455
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

country_destination
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
NDF
54850 
GB
52688 
ES
50508 
NL
47598 
US
47539 
Other values (7)
293096 

Length

Max length5
Median length2
Mean length2.346165604
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNDF
2nd rowUS
3rd rowother
4th rowUS
5th rowUS

Common Values

ValueCountFrequency (%)
NDF54850
10.0%
GB52688
9.6%
ES50508
9.2%
NL47598
8.7%
US47539
8.7%
PT47100
8.6%
other44751
8.2%
FR43870
8.0%
CA42542
7.8%
IT40200
7.4%
Other values (2)74633
13.7%

Length

2021-10-23T01:55:16.823411image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ndf54850
10.0%
gb52688
9.6%
es50508
9.2%
nl47598
8.7%
us47539
8.7%
pt47100
8.6%
other44751
8.2%
fr43870
8.0%
ca42542
7.8%
it40200
7.4%
Other values (2)74633
13.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2021-10-23T01:55:05.595554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:03.563990image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:07.163484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:10.378745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:13.601242image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:17.146319image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:21.133683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:24.354798image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:27.836186image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:31.476113image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:34.757746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:37.995504image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:41.611718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:45.273915image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:48.707880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:52.021397image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:55.279878image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:58.519850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:55:01.837100image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:55:05.760973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:03.860676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:07.352256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:10.570836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:13.771760image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:17.307681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:21.299135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:24.529750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:28.023630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:31.648644image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:34.921252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:38.164997image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:41.778117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:45.446932image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:48.877374image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:52.184607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:55.443879image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:58.684458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:55:02.004349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:55:05.928792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:04.032256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:07.526972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:10.733111image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:13.955810image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2021-10-23T01:54:57.316521image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:55:00.638190image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:55:03.988181image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:55:07.844438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:06.086581image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:09.358223image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:12.589192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:16.102993image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:19.457661image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:23.337517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:26.749044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:30.430517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:33.734575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:36.965350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:40.523668image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:43.911608image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:47.535163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:50.967408image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:54.259354image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:57.489550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:55:00.810958image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:55:04.169087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:55:08.020382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:06.285891image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:09.536538image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:12.757929image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:16.274135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:19.629673image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:23.508966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:26.927161image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:30.607216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:33.906062image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:37.136821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:40.701937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:44.084926image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:47.713336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:51.153928image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:54.429568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:57.662059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:55:00.984416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:55:04.354154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:55:08.189257image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:06.485611image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:09.709370image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:12.926224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:16.458591image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:20.285468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:23.680223image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:27.110023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:30.786338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:34.076138image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:37.307602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:40.876807image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:44.253746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:47.888129image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:51.331682image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:54.599301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:57.828660image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:55:01.152849image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:55:04.535150image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:55:08.357651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:06.658623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:09.872757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:13.090137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:16.623066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:20.602214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:23.845477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:27.294756image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:30.963458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:34.243280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:37.478498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:41.052948image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:44.421407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:48.056516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:51.506305image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:54.767172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:57.995869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:55:01.320089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:55:04.701911image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:55:08.528213image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:06.830930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:10.041432image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:13.257944image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:16.796128image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:20.796621image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:24.012737image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:27.468876image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:31.135738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:34.409311image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:37.648860image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:41.235456image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:44.593740image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:48.362580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:51.676051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:54.939665image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:58.162837image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:55:01.495070image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:55:05.252645image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:55:08.703921image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:07.001793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:10.211159image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:13.438036image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:16.968832image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:20.967869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:24.186575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:27.652318image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:31.307483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:34.583478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:37.823882image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:41.437870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:44.768392image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:48.537883image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:51.847842image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:55.110479image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:54:58.340466image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:55:01.665753image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-23T01:55:05.428500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-10-23T01:55:16.914711image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-23T01:55:17.097894image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-23T01:55:17.280988image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-23T01:55:17.449880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-10-23T01:55:17.595010image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-23T01:55:09.064119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-23T01:55:10.334802image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

agesignup_flowdays_from_first_active_until_bookingdays_from_first_active_until_account_createddays_from_account_created_until_first_bookingyear_first_activemonth_first_activeday_first_activeday_of_week_first_activeweek_of_year_first_activeyear_first_bookingmonth_first_bookingday_first_bookingday_of_week_first_bookingweek_of_year_first_bookingyear_account_createdmonth_account_createdday_account_createdday_of_week_account_createdweek_of_year_account_createdgendersignup_methodlanguageaffiliate_channelaffiliate_providerfirst_affiliate_trackedsignup_appfirst_device_typefirst_browsercountry_destination
038022287321496200952352120156290272011525221MALEfacebookenseogoogleuntrackedWebMac DesktopChromeNDF
1563419476-572009691242010820312010928139FEMALEbasicendirectdirectuntrackedWebWindows DesktopIEUS
24201043765278200910315442012985362011125049FEMALEfacebookendirectdirectuntrackedWebMac DesktopFirefoxother
341072280-20820091281502010218372010914137-unknown-basicendirectdirectuntrackedWebMac DesktopChromeUS
446030320101255320101511201012553FEMALEbasicenothercraigslistuntrackedWebMac DesktopSafariUS
547010010201013653201011322201013653FEMALEbasicendirectdirectomgWebMac DesktopSafariUS
6500206020620101401201072933020101401FEMALEbasicenothercraigslistuntrackedWebMac DesktopSafariUS
7460000201014012010140120101401-unknown-basicenothercraigslistomgWebMac DesktopFirefoxUS
8360202201014012010162120101401FEMALEbasicenothercraigslistuntrackedWebMac DesktopFirefoxUS
947020010200120101511201562902720101511FEMALEbasicenothercraigslistuntrackedWebiPhone-unknown-NDF

Last rows

agesignup_flowdays_from_first_active_until_bookingdays_from_first_active_until_account_createddays_from_account_created_until_first_bookingyear_first_activemonth_first_activeday_first_activeday_of_week_first_activeweek_of_year_first_activeyear_first_bookingmonth_first_bookingday_first_bookingday_of_week_first_bookingweek_of_year_first_bookingyear_account_createdmonth_account_createdday_account_createdday_of_week_account_createdweek_of_year_account_createdgendersignup_methodlanguageaffiliate_channelaffiliate_providerfirst_affiliate_trackedsignup_appfirst_device_typefirst_browsercountry_destination
546269460172017220141965201472723020141965MALEbasicendirectdirectuntrackedWebMac DesktopChromeother
546270250000201312171512013121715120131217151MALEbasicensem-brandgoogleomgWebWindows DesktopChromeother
5462712920730732014342112014516521201434211FEMALEbasicendirectdirectuntrackediOSMac DesktopChromeother
546272310101201311191472013112024720131119147FEMALEbasicensem-non-brandgoogleomgWebWindows DesktopChromeother
546273400101201210104020121022402012101040FEMALEbasicendirectdirectomgWebMac DesktopSafariother
546274330101201332021220133224122013320212FEMALEbasicendirectdirectlinkedWebMac DesktopFirefoxother
546275340520522012770272012829335201277027MALEfacebookendirectdirectlinkedWebWindows DesktopFirefoxother
54627629022022201352122120136133242013521221-unknown-basicendirectdirectuntrackedWebWindows DesktopIEother
546277280202201411303201411633201411303FEMALEbasicendirectdirectlinkedWebMac DesktopSafariother
546278390101201172252920117233292011722529FEMALEbasicendirectdirectuntrackedWebWindows DesktopChromeother

Duplicate rows

Most frequently occurring

agesignup_flowdays_from_first_active_until_bookingdays_from_first_active_until_account_createddays_from_account_created_until_first_bookingyear_first_activemonth_first_activeday_first_activeday_of_week_first_activeweek_of_year_first_activeyear_first_bookingmonth_first_bookingday_first_bookingday_of_week_first_bookingweek_of_year_first_bookingyear_account_createdmonth_account_createdday_account_createdday_of_week_account_createdweek_of_year_account_createdgendersignup_methodlanguageaffiliate_channelaffiliate_providerfirst_affiliate_trackedsignup_appfirst_device_typefirst_browsercountry_destination# duplicates
50458350000201361352720136135272013613527-unknown-basicendirectdirectuntrackedWebMac DesktopChromePT89
32942310000201361552420136155242013615524FEMALEfacebookendirectdirectuntrackedWebWindows DesktopChromePT85
42373330000201361562420136156242013615624-unknown-basicensem-brandgoogleuntrackedWebWindows DesktopChromePT68
37723320000201361552420136155242013615524-unknown-basicensem-brandgoogleuntrackedWebWindows DesktopChromePT66
27502300000201281423320128142332012814233FEMALEfacebookendirectdirectlinkedWebMac DesktopChromePT65
11205260000201334010201335110201334010-unknown-basicendirectdirectuntrackedWebMac DesktopChromePT64
46652340000201361562420136156242013615624-unknown-basicendirectdirectlinkedWebMac DesktopFirefoxPT63
8217250000201335110201336210201335110MALEfacebookensem-brandgoogleomgWebWindows DesktopChromePT62
43050330101201310334020131044402013103340MALEbasicendirectdirectuntrackedWebWindows DesktopChromePT62
53577360000201261812420126181242012618124FEMALEfacebookendirectdirectomgWebMac DesktopSafariPT60